mcp-server-llmling

# mcp-server-llmling [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://pypi.org/project/mcp-server-llmling/) [](https://github.com/phil65/mcp-server-llmling/releases) [](https://github.com/phil65/mcp-server-llmling/graphs/contributors) [](https://github.com/phil65/mcp-server-llmling/discussions) [](https://github.com/phil65/mcp-server-llmling/forks) [](https://github.com/phil65/mcp-server-llmling/issues) [](https://github.com/phil65/mcp-server-llmling/pulls) [](https://github.com/phil65/mcp-server-llmling/watchers) [](https://github.com/phil65/mcp-server-llmling/stars) [](https://github.com/phil65/mcp-server-llmling) [](https://github.com/phil65/mcp-server-llmling/commits) [](https://github.com/phil65/mcp-server-llmling/releases) [](https://github.com/phil65/mcp-server-llmling) [](https://github.com/phil65/mcp-server-llmling) [](https://github.com/phil65/mcp-server-llmling) [](https://github.com/phil65/mcp-server-llmling) [](https://codecov.io/gh/phil65/mcp-server-llmling/) [](https://github.com/psf/black) [](https://pyup.io/repos/github/phil65/mcp-server-llmling/) [Read the documentation!](https://phil65.github.io/mcp-server-llmling/) # LLMling Server Manual ## Overview mcp-server-llmling is a server for the Machine Chat Protocol (MCP) that provides a YAML-based configuration system for LLM applications. [LLMLing](https://phil65.github.io/LLMling/), the backend, provides a YAML-based configuration system for LLM applications. It allows to set up custom MCP servers serving content defined in YAML files. - **Static Declaration**: Define your LLM's environment in YAML - no code required - **MCP Protocol**: Built on the Machine Chat Protocol (MCP) for standardized LLM interaction - **Component Types**: - **Resources**: Content providers (files, text, CLI output, etc.) - **Prompts**: Message templates with arguments - **Tools**: Python functions callable by the LLM The YAML configuration creates a complete environment that provides the LLM with: - Access to content via resources - Structured prompts for consistent interaction - Tools for extending capabilities ## Key Features ### 1. Resource Management - Load and manage different types of resources: - Text files (`PathResource`) - Raw text content (`TextResource`) - CLI command output (`CLIResource`) - Python source code (`SourceResource`) - Python callable results (`CallableResource`) - Images (`ImageResource`) - Support for resource watching/hot-reload - Resource processing pipelines - URI-based resource access ### 2. Tool System - Register and execute Python functions as LLM tools - Support for OpenAPI-based tools - Entry point-based tool discovery - Tool validation and parameter checking - Structured tool responses ### 3. Prompt Management - Static prompts with template support - Dynamic prompts from Python functions - File-based prompts - Prompt argument validation - Completion suggestions for prompt arguments ### 4. Multiple Transport Options - Stdio-based communication (default) - Server-Sent Events (SSE) for web clients - Support for custom transport implementations ## Usage ### With Zed Editor Add LLMLing as a context server in your `settings.json`: ```json { "context_servers": { "llmling": { "command": { "env": {}, "label": "llmling", "path": "uvx", "args": [ "mcp-server-llmling", "start", "path/to/your/config.yml" ] }, "settings": {} } } } ``` ### With Claude Desktop Configure LLMLing in your `claude_desktop_config.json`: ```json { "mcpServers": { "llmling": { "command": "uvx", "args": [ "mcp-server-llmling", "start", "path/to/your/config.yml" ], "env": {} } } } ``` ### Manual Server Start Start the server directly from command line: ```bash # Latest version uvx mcp-server-llmling@latest ``` ### 1. Programmatic usage ```python from llmling import RuntimeConfig from mcp_server_llmling import LLMLingServer async def main() -> None: async with RuntimeConfig.open(config) as runtime: server = LLMLingServer(runtime, enable_injection=True) await server.start() asyncio.run(main()) ``` ### 2. Using Custom Transport ```python from llmling import RuntimeConfig from mcp_server_llmling import LLMLingServer async def main() -> None: async with RuntimeConfig.open(config) as runtime: server = LLMLingServer( config, transport="sse", transport_options={ "host": "localhost", "port": 8000, "cors_origins": ["http://localhost:3000"] } ) await server.start() asyncio.run(main()) ``` ### 3. Resource Configuration ```yaml resources: python_code: type: path path: "./src/**/*.py" watch: enabled: true patterns: - "*.py" - "!**/__pycache__/**" api_docs: type: text content: | API Documentation ================ ... ``` ### 4. Tool Configuration ```yaml tools: analyze_code: import_path: "mymodule.tools.analyze_code" description: "Analyze Python code structure" toolsets: api: type: openapi spec: "https://api.example.com/openapi.json" namespace: "api" ``` ## Server Configuration The server is configured through a YAML file with the following sections: ```yaml global_settings: timeout: 30 max_retries: 3 log_level: "INFO" requirements: [] pip_index_url: null extra_paths: [] resources: # Resource definitions... tools: # Tool definitions... toolsets: # Toolset definitions... prompts: # Prompt definitions... ``` ## MCP Protocol The server implements the MCP protocol which supports: 1. **Resource Operations** - List available resources - Read resource content - Watch for resource changes 2. **Tool Operations** - List available tools - Execute tools with parameters - Get tool schemas 3. **Prompt Operations** - List available prompts - Get formatted prompts - Get completions for prompt arguments 4. **Notifications** - Resource changes - Tool/prompt list updates - Progress updates - Log messages